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Animal spirits and the business cycle: Empirical evidence from moment matching

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  • Jang, Tae-Seok
  • Sacht, Stephen

Abstract

In this paper we empirically examine a hybrid New-Keynesian model with heterogeneous bounded rational agents who may adopt an optimistic or pessimistic attitude - so called animal spirits - towards future movements of the output and inflation gap. The model is estimated via the simulated method of moments using Euro Area data from 1975Q1 to 2009Q4. In addition, we compare its empirical performance to the standard model with rational expectations. Our empirical results show that the model-generated auto- and cross-covariances of the output gap, the inflation gap and the nominal interest gap can provide a good approximation of the empirical second moments. The result is mainly driven by a high degree of persistence in the output and inflation gap due to the impact of animal spirits on economic activity. Furthermore, over the whole time interval the agents had expected moderate deviations of the future output gap from its steady state value.

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  • Jang, Tae-Seok & Sacht, Stephen, 2014. "Animal spirits and the business cycle: Empirical evidence from moment matching," Economics Working Papers 2014-06, Christian-Albrechts-University of Kiel, Department of Economics.
    • Handle: RePEc:zbw:cauewp:201406
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    Cited by:

    1. Jang, Tae-Seok & Sacht, Stephen, 2017. "Modeling consumer confidence and its role for expectation formation: A horse race," Economics Working Papers 2017-04, Christian-Albrechts-University of Kiel, Department of Economics.
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    3. Bas Aarle & Cindy Moons, 2017. "Sentiment and Uncertainty Fluctuations and Their Effects on the Euro Area Business Cycle," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(2), pages 225-251, November.
    4. Kukacka, Jiri & Jang, Tae-Seok & Sacht, Stephen, 2018. "On the estimation of behavioral macroeconomic models via simulated maximum likelihood," Economics Working Papers 2018-11, Christian-Albrechts-University of Kiel, Department of Economics.
    5. Jump, Robert Calvert & Levine, Paul, 2019. "Behavioural New Keynesian models," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 59-77.
    6. Jang, Tae-Seok & Sacht, Stephen, 2021. "Forecast heuristics, consumer expectations, and New-Keynesian macroeconomics: A Horse race," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 493-511.
    7. Tae-Seok Jang & Stephen Sacht, 2022. "Macroeconomic dynamics under bounded rationality: on the impact of consumers’ forecast heuristics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(3), pages 849-873, July.
    8. Zila, Eric & Kukacka, Jiri, 2023. "Moment set selection for the SMM using simple machine learning," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 366-391.
    9. Franke, Reiner, 2022. "An empirical test of a fundamental Harrod-Kaldor business cycle model," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 1-14.
    10. Özge Dilaver & Robert Calvert Jump & Paul Levine, 2018. "Agent‐Based Macroeconomics And Dynamic Stochastic General Equilibrium Models: Where Do We Go From Here?," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1134-1159, September.
    11. Lux, Thomas, 2024. "Lack of identification of parameters in a simple behavioral macroeconomic model," Economics Working Papers 2024-02, Christian-Albrechts-University of Kiel, Department of Economics.
    12. Liang, Hanchao & Yang, Chunpeng & Cai, Chuangqun, 2017. "Beauty contest, bounded rationality, and sentiment pricing dynamics," Economic Modelling, Elsevier, vol. 60(C), pages 71-80.
    13. Kukacka, Jiri & Sacht, Stephen, 2023. "Estimation of heuristic switching in behavioral macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    14. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    15. Liang, Hanchao & Yang, Chunpeng & Zhang, Rengui & Cai, Chuangqun, 2017. "Bounded rationality, anchoring-and-adjustment sentiment, and asset pricing," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 85-102.
    16. Jang Tae-Seok, 2020. "Animal spirits in an open economy: an interaction-based approach to the business cycle," The B.E. Journal of Macroeconomics, De Gruyter, vol. 20(1), pages 1-16, January.
    17. Xu, Xin & Xu, Xiaoguang, 2023. "Monetary policy transmission modeling and policy responses," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    18. Christian Schoder, 2017. "Estimating Keynesian models of business fluctuations using Bayesian Maximum Likelihood," Review of Keynesian Economics, Edward Elgar Publishing, vol. 5(4), pages 586–630-5, October.

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    More about this item

    Keywords

    Animal Spirits; Bounded Rationality; New-Keynesian Model; Simulated Method of Moments;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E12 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Keynes; Keynesian; Post-Keynesian; Modern Monetary Theory
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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